Cybersecurity and Cognitive Science provides the reader with multiple examples of interactions between cybersecurity, psychology and neuroscience. Specifically, reviewing current research on cognitive skills of network security agents (e.g., situational awareness) as well as individual differences in cognitive measures (e.g., risk taking, impulsivity, procrastination, among others) underlying cybersecurity attacks. Chapters on detection of network attacks as well as detection of cognitive engineering attacks are also included. This book also outlines various modeling frameworks, including agent-based modeling, network modeling, as well as cognitive modeling methods to both understand and improve cybersecurity.
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Table of Contents
Part I: Social Engineering, Security, and Cyber Attacks 1. Social Engineering Attacks and Defenses in the Physical World vs. Cyberspace: A Contrast Study 2. A Dual Integrated Dynamic Intrusion Detection System (DID-IDS) for Protection Against Network and Social Engineering Attacks 3. Working from Home Users at Risk of the COVID-19 Ransomware Attacks 4. Individual Differences in Cyber Security Behavior Using Personality Based Models to Predict Susceptibility to Sextortion Attacks 5. The Development of a Logic for Capturing Mismorphisms: Deconstructing Security and Privacy Issues
Part II: Behavioral Studies of Cybersecurity 6. Are you Anonymous? Social Psychological Processes of Hacking Groups 7. On the Relation Between Hacking and Autism or Autistic Traits: A Systematic Review of the Scientific Evidence 8. An Introduction to Cyberbullying 9. The Impact of Cyberbullying Across the Lifespan 10. Cyber Situational Awareness Issues and Challenges 11. Development and Application of the Information Security Core Human Error Causes (IS-CHEC) Technique
Part III: Machine Learning and Modeling� Applications to Cybersecurity 12. Machine Learning for the Security of Healthcare Systems Based on Internet of Things and Edge� Computing? 13. Lying Trolls: Detecting Deception and Text-Based Disinformation Using Machine Learning 14. Modeling the Effects of Network Size in a Deception Game Involving Honeypots 15. Computational Modeling of Decisions in Cyber-Security Games in the Presence or Absence of Interdependence Information
